Principals and student outcomes: Evidence from U.S. high schools

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Principals and Student Outcomes: Evidence From U.S. High Schools DOMINIC Depnrtm~nt

of Labor

J. BREWER

Economics. New York Stnte School of Industrial University. Ithaca. NY 11X53. U.S.A.

and Labor

Reintions.

Cornell

Abstract - This paper prewnt\ an empirical analysi\ of the effect> of principals on public high school students’ academic achievement. using HighSc/m~/ rrurl B~YY~,I~/. Despite policy relevance. previous qualitative and quantitative research provides little systematic evidence on principal cffccts. at least for high school>. Principal characteristics and variables decigned to capture less tangible aspects of the principal’s role are included in educationnl production functions. The results suggest principals do have ;I me:lsurable imr>nct on student achievement. through the selection of teachers and setting of acxicmically oriented school peals. I.

MEASURES

INTRODUCTION

DESIGNED

to enhance

principal authority recent school reform proposals, including those adopted for Chicago (Moore. 1YYO). However, while many recent “effective schools” case studies have noted the importance of school leadership and organizational characteristics, only Eberts and Stone (IYXX) have obtained direct statistical evidence on whether principal behavior affects student achievement. Using a national sample of elementary schools. they find that principals are important for student achievement via instructional leadership and conflict resolution. In this paper. I attempt to confirm the finding that “principals matter” for high school students. using data from High Scl~ool rmtl B~JWIK~ (HSB). I estimate educational production functions that include principal characteristics. as well as other variables constructed to capture less tangible aspects of the principal’s role. In particular, I demonstrate that principals’ selection of teachers. and setting of clear and academically oriented school goals. influences student achievement gains. These results have potentially important public policy implications. The paper is set out as follows. In section II, I have

been

[Manuscript

prominent

in

received 3 June

LW?:

discuss the ways in which principals might affect student achievement. and review existing evidence. Section III outlines the empirical framework and describes the HSB data used here. The results are presented in section IV. Section V provides a conclusion.

II. PRINCIPALS AND STUDENT ACHIEVEMENT: EXISTING EVIDENCE The mechanisms through which principals influence students are complex and defy simple categorization. At one level principals may impact individual students by serving as their mentor or role model. Almost no attention has been given to this issue; the size of most public high schools and time demands of the job means principals are usually viewed as being removed from direct contact with most students (Donaldson, IYYI; Morris er nl., 19X4). At the other extreme, principals may indirectly affect all students merely by ensuring schools run smoothly on a day to day basis. “Clear and consistent school rules and policies tend to improve the general disciplinary climate of the school. and contribute to improved staff and student morale” (Bryk cf nl.. IYXY. p. 45).

revision ;iccrptrd for publication I I 281

November

19Y2.1

282

Economics

The

principalship

and demanding. preparation

is evidently

Most

principals

not

The

been

Pautler.

multidimensional

accomplished? One way is simply ment

formal

required for certification)

importance

of principal

systematically

for

training

examined

(Heller

a large part of the principal’s

spent dealing with these agencies. This

through

literature which

time is

bureaucratic

affect

teacher selection and motivation. the curriculum.

First,

principals

role in the selection active involvement

in the hiring a selection

to making

supplied

by the district.

ranges from

process from

public school principals good faculty control. ation

However.

between

authority.

greater informal

tactical manipulation example.

Wise

cipals inherit

Chubb how office vari-

of formal

influence

and

of rules that may exist (see. for

resources

while

on appointment.

prin-

over time

their ability to influence quality and composition the faculty is likely Second.

role in establishing

a sense of school purpose

his or her “vision”

(Concoran,

1985: Bartell,

LeSourd

and Grady. bringing

curriculum

design.

teaching techniques

and

to staff and parents

1990: Rosenholtz.

1990). This

IYX5:

serves to motivate

about

increased

class

preparation.

and so on. While

best

and policy

A

third

way

principals over

what

effect&

previous

attention

to

innovative case studies

spent

administration.

and

formulation principals transmit

in

had weak effects on

instruction.

in-service

preparation.

programs.

While

of clear educational goals is important. with

academically

oriented

these to their teachers are likely

most impact on student achievement.’

goals

who

to have the How

and “facilihave been for

to simultane-

takes

school

design

place

in

literature

curriculum

belicvc

they

is this

involves

the

the supervision

and the development of

graduation

De Bevoise,

lYX7; Firestone

The

that “out-

are instructional

strategies.

and

IYXX:

Murphy.

classroom.

IYYO. p. 127). This

of instructional

(Murphy.

the

has stressed

and evaluation of programs. the

have suggested

is through direct control

requirements

10X1; Hallinger

and Wilson,

IYXS).

and

It is not

clear that effective schooling is associated uniformly with

each of

example.

thcsc

tive schools

studies

to be important Pautler

dimensions,

Concoran’s

summary

however.

shows curriculum

organization

(IYYO) suggest that instructional

may conflict functions

with

other

For

of five major effec-

in just one (19X5. p. X4). Hcller managerial

and

Icadership

and political

of the principal.

In addition.

it is uncertain whether

instructional

leadership /IU se has a po.siti\~e impact on students. school’s

intcrvcntion program.

may bring

teachers as unnecessary There

intcrferencc

(IYXX)

leadership”.

independent

finding

in Eberts

that “active principal

of “instructional

leader-

had a qyrti~~c~ effect on student achicvcment.

Similarly. mediocre

an

active

principal

academic goals

changes

as narrowly

is

the setting

to

have

come a popular

much

by standard

tests.

“instructional

or of

a

at least Hence.

is more exactly correlated

with

as well as “strong

Icadet-ship” (Rowan

Although

implements

curriculum

achievement.

of “high standards”

programmatic

who

through

unlikely

measured

effective schooling

23).

to a

in their tasks.

is some evidence for this assertion

and Stone’s ship”.

coherency

but it might also be viewed by

beneficial effect on students‘

and Stone (IYXX) found that principal charactime

makers

studies

impact students

Eberts

(and other variables)

to do this

leaders employ-

need for teacher autonomy and teamwork?

other

teristics

way

with

there is

it is not uncommon

have cited evidence that this does indeed take place.

teacher

However.

“directive”

Indeed.

both researchers

Principal

appears to have a pivotal

transmitting

teachers.

of

to increase.

the principal

cffcctive.’

principals

are able to establish

et cd.. 1987). Likewise.

found

(Bartell.

central

power via personal

principal’s

ing an aggressive management strategy.

leaders”

and tenure

on the extent

principals

the

the

a consensus

tative” Icaders emphasizing consultations

standing

are hampered in attracting

districts

on

19X.5). Both

list

while there is considerable

many

evidence

the

have stressed

because of excessive

clear

from

teachers. Many principals

for example.

is by establishing

a short

and school board interference.

and Moe (1990).

over

or the power to block the

appointment of particular district

via

and control

are inhibited by the presence of seniority rules,

is

play an important

outset.

through appoint-

share

ously emphasize strong principal leadership with the

students

of teachers. This

who

teachers over what is important. no

or

faculty

goals. Another

(Manassc,

suggests that the major route

principals

new

has

role has only an indirect impact on students. Previous

of

and

1990). Since many aspects of school policy

arc determined by the school board. or at district state level.

Review

have little

(teaching experience plus some coursc-

work in administration the task.

of Educntion

ct trl..

lYX.3. p.

leadership”

has bc-

label, its precise meaning remains

somewhat clusivc (Murphy.

IYXX).

Principuls and Student Outcomes Despite all the attention given to internal school organization during the past decade, there is little systematic evidence relating student achievement and principal behavior through the channels discussed above.’ Most research has employed qualitative analysis of schools through interviews and observation of teachers and administrators, an approach which makes strong causal inferences problematic (Rowan et al.. 19X3; Manasse, 198.5). Reliance on small samples, and focusing on outlier schools (i.e. those identified in advance as outstanding or poor). exaggerates the uniqueness of each school. and raises serious questions about the gcncralizability of the findings. Although there have been several attempts to model school organization, the only previous study dealing explicitly with principals within a production function framework is Eberts and Stone (lY88) (ES).” They use data on nearly 15.000 elementary school students, explicitly supplementing standard student. school and district variables in an educational production function, by principal characteristics and principal and teacher “perception” variables. They use the average of teacher and principal perceptions of whether the principal is an “active leader” and involved in the mathematics curriculum (instructional leadership), plus the joint perceptions of whether the principal is effective in conflict resolution, and of whether principal and teachers work well together.” The degree of disagreement on these four dimensions (captured by the difference between teacher and principal views, multiplied by the absolute value of the difference, giving larger disparities more weight) are entered as additional regressors.’ The authors conclude that “principal leadership in instructional activities and in conflict resolution are clearly important to student achievement. but strong leadership outside these areas is not; divergence of opinion (loss of consensus) bctwcen teachers and the principal over the principal’s effectivcncss in resolving conflicts also appears to retard student achievement” (p. 298). They also report that :I principal’s teaching and administrative experience is positively and significantly rclatcd to student achievement. but the highest degree level of the principal is negatively related. The approach taken here is similar to ES with a few important differences. First, ES examine elementary school principals, whereas I use data on high schools principals. Although high schools

283

typically have more students, are more departmentalized, and have larger administrative staffs than elementary schools, it is not clear u priori how this might translate into differences in estimates of principal effects on student achievement (Firestone 1982). Second, ES are unable and Herriot, (presumably due to data limitations) to capture the effect of principals on student achievement through teacher selection or the setting of school goals. This is an important omission which I rectify here. Third. as noted in the next section, the HSB data used here preclude the inclusion of some ES variables in my analysis. III. EMPIRICAL

FRAMEWORK

AND DATA

To formally determine whether principals have effects on student achievement I estimate educational production functions using multivariate regression. Following the “Coleman Report” (Coleman et al., 1966), a vast number of empirical studies have used this approach to determine the effects of family. school and community characteristics on student achievement.” While there are conceptual and statistical problems with this model, it has the advantage of simplicity.” To the familiar model I add several variables designed to capture possible principal effects discussed above. The data are from HSB. a national longitudinal survey conducted for the U.S. Department of Education, 1980-1986. The original sample consisted of up to 36 sophomores and 36 seniors from II00 high schools, who were administered a series of cognitive tests measuring verbal and quantitative ability in 1980. A sub-sample of the sophomores was subsequently retested in their senior year, permitting an investigation of the determinants of the change in achievement for each student between the base and follow-up years. The sample is confined to public school students.“’ This, and other data constraints, leaves a sample of 2070 public school students.” The outcome variable is the change in student test scores between sophomore (1980) and senior (1982) years (GAIN).” In general. gain scores are preferred to test score levels because unobservables such as parental investment in the home, unmeasured school quality. or intrinsic student ability, which influence the test score levels but do not change over time, are eliminated in the gain specification. In each model. I include the base year test score (BYTEST) on the right hand side.13 One

284

Economics

of Education

caution is that gain score equations may be subject to sample selection bias since they exclude students who have dropped out of high school between their sophomore and senior year (Ehrenberg and Brewer, forthcoming). Prior research that has used HSB does not correct for this possible bias, and I do not present selectivity corrected estimates here.“’ Explanatory variables included in the analyses to control

for

an

in~ivi~u~~1.s

family

b~l~k~r(~un~

pupil district centage variable effects

is unknown

(INCUN).

or

or

md

PTACL

students

definitions

and in the

and [standard

deviations

include teaching

and sample means

1.

Scmey

each

come from the A~~~~j~~i.st~f~t~~~ (ATS)

principal’s

(PTEACH),

administration

as a supple-

conducted

other

years

I

experience

in

and years of experience

in

than

of

3s

principal

(PADMIN).‘~ Perhaps the major

channel of principal

on student achievement ation of teachers. number their

1. Variable

for continuous

low

for each school

is via selection

I include in each specification

of years each principal

current

influence and motiv-

school

(PEXPSCH),

the

has been head of and

the

per-

definitions

Base year standardized test score (49.92) [X.Y8] Follow up standardized test score (50.61) [Y.OS] Gain score: FUTEST - BYTEST (0.69) (4.741 Dummy for gender of student. female = I (0.50) Dummy for race of student. black = 1 (0.10) Dummy for ~thnicity of student, Hispanic = 1 (0. IY) Family IncOme of student. $‘000 (21.58) [ 18.7Oj Dummy for family income unknown = I (0.21) Family size of student (3.28) [I ,6Y] Father’s years of education (X.90) [6.40] Dummy for father’s education unknown = I (0.17) Mother‘s years of education (9.98) [-5.S] Dummy for mother’s education unknown = 1 (O.lh) Mean class size (23.34) [3.53] Total school membership, ‘00lfs (1.29) [0.78] School district Der nut%1 exnenditurc. $‘OOOs (I .SS) lO.hS] Percentage of &t&n& in low SES quartile (jO.74) _ Percentage of teachers at school IO or more years (42.24) [20.96] Mean teacher salary. $‘OOOs (22.33) [4. Ih] Principal’s years of teaching experience (14.20) [Y.O3] Princinal’s vears of ~ldministr~ltive experience (4.87) iS.OIl Prim&l’s iears as principal of current school’(6.hj< [S.28] Principal‘s relative salary: principal’s salary divided by TSALARY (1.76) [0.‘_4] Dummy for high self reported principal influence in determining methods of instruction = Dummy for high self reported principal influence in determining the curriculum = I (0.3.5) Dummv for high principal ranking of academic IWIIS for school = I (0.25) Dumm; for I& &inci&l ranking-of academic &Is for school = 1 (0.07) Pcrcentaee of teachers annointed during tenure of school’s current principztl (51.56) /%.X3] Percent;& of teachers ~~p~~inted duri& tenure of school’s current. hifih zcademif go&. (PTOTCH x ADUMH) (13.07) [26.12] Percentage of teachers appointed during tenure of school’s current. low academic goal\. (PTOTCH x ADUML) (3.67) [ 14.773

Sampie (means)

peer

ment to HSB in 1984 in around 320 public schools.

is unknown (MEDUN and DEDUN, respectively). School characteristics include school size (SCHSIZE), average class size (CLASS), per

GAIN FEMALE BLACK HISPANIC INCOME .lNCUN FAMSIZE EDUCD DEDUN EDUCM MEDUN CL.ASS SCHSIZE PPDEXP PCTLOW TEXP TSALARY PTEACH PADMIN PEXPSCH PRE:LSAL 1DUM CDUM ADUMH ADlJML PTOTCH PTACH

Tencher

of

An additi~~n~ll

wealth

status (SES) quartile Variable

and the peryears cxperi-

(TEXP).”

percentage

Data on principals

arc

education

BYTEST FUTEST

is the

(PCTLOW).

father’s

Table

(PPDEXP), IO or more

community

are given in Table

where family income mother’s

capturing

socioeconomic

income

variables (= I) for observations

expenditure

of teachers with

cnce at the current school

size (INCOME). family (FAMSIZE), and mother’s and father’s years of education (EDUCM and EDUCD. respectively), as well as dummy variables for gender (FEMALE), race (BLACK) and ethnicity (HISPANIC). In order to maximize the sample size I define dunlmy family

Review

variables]

are shown in parentheses.

I (0.21)

prill~ip~ll principal

Principals

and Student

centagc of total faculty appointed during their tenure as principal (PTOTCH).” The coefficient of PTOTCH is expected to be positive (holding all clsc. including principal tenure and teacher experience. constant) since via selection and motivation, the more teachers appointed by the current principal. the more in tune they should be with the principal’s leadership style, work methods and goals. On familiarity grounds the school should operate more efficiently the higher is PTOTCH. A stronger positive effect would be expected if principals appoint good quality teachers, but this is difficult to measure. One possibility is that principals who stress academic excellence appoint and motivate teachers who are likely to carry out this school goal, and this should have a positive affect on academic performance. ADUMH and students’ ADUML are dummy variables indicating if the principal ranked academic excellence as a high or low school goal, respectively.” PTACH and PTACL are interaction terms created by multiplying PTOTCH by ADUMH and ADUML, respectively. The higher the percentage of teachers appointed by a principal with high academic goals (PTACH), the greater the student gain score is expected to be; conversely, the higher the percentage of teachers appointed by a principal with low academic goals (PTACL) the lower the gain score. Two dimensions of principal “instructional leadership” are captured in the analysis. In ATS. principals were asked how much influence they had in determining instructional methods, and in establishing the curriculum. using a qualitative scale. Using these responses. dummy variables were created indicating if the principal thought they had a great deal of influence over instructional methods (IDUM) and curriculum content (CDUM).‘” If the findings of the effective schools literature are replicated across schools, both these variables should have a positive impact on student test score gains. Although conventional theory does not yield production functions which include factor prices as arguments. numerous studies have included teacher salaries as explanatory variables in the empirical specification of student achievement models.“’ There are several reasons for doing so. Salaries are an important manipulable policy variable. While teacher and administrator salaries are usually determined on the basis of years of experience and degree level, they may also reflect unobservable quality dimensions of these inputs not captured by other

28.5

Outcomes

variables. Theoretically, efficiency wage theory provides a justification for their inclusion: higher salaries. independent of experience, may raise work effort and/or attract higher quality faculty. Hence. in some specifications reported below, I include mean teacher salary (TSALARY) and PRELSAL, the salary of the principal relative to the mean teacher salary in the school.” While a higher relative salary is expected to have a positive impact on student achievement, the source of this impact (for example, unobserved quality or effort-raising) cannot be determined with the available data. This issue is discussed further in section IV. It should be noted that there are several important omissions from the specifications reported in the next section. For example, due to data limitations, it is not possible to include the effects of teacher’s time allocation (to instruction, preparation and administration) in the estimated models, nor to accurately capture the principal’s ability to identify conflict, which ES found to be significantly related to achievement. While one might hypothesize that these variables would be as important at high school level as at elementary level, it is not clear how their omission might affect the coefficients of the included variables. IV. RESULTS Results of various specifications arc presented in Table 2. Columns (I), (2) and (3) show educational production functions with principal variables, but exclude teacher and principal salary (TSALARY and PRELSAL) variables. The same models are shown with these included. in columns (4), (5) and (6).” Individual and family variables have signs consistent with previous research: female, black and Hispanic students have lower test score gains; higher family income and parental education levels raise gain scores. Higher base year test scores are associated with lower test score gains.‘3 Across models. school size (SCHSIZE) remains significant and positive, and class size (CLASS) negative.” However, teacher experience and per pupil district expenditures have no statistically significant effect on gain scores. Turning to principal neither principal experience in characteristics. teaching (PTEACH) or administration (PADMIN) is significant, and the coefficient of PADMIN is actually negative. These results are in contrast to ES. who found both measures had a small positive,

286

Economics Table 2. Education

production

functions

of Education

Review

with principal

variables

(absolute

Gain (2)

(1) Y.64

(3)

-0.IH6 -0.23’) -0.73x -0.065 0.00s 0.105 0.027 0.0%

(7.Y) (13.2) (1.2) (2.1) (3.3) (0.6) (1.X) (1.2) (1.6)

7.64 -0. IX6 -0.230 -0.x33 -0.Y63 0.004 0. I I I 0.02x 0.055

(7.X) (14.3) (1.2)

CLASS SCHSIZE PPDEXP PCTLO W TEXP TSALARY

-0.076 0.675 0.205 0.000 -0.004

(2.2) (4.2) (1.2) (0. I) (0.X)

PTEACH PADMlN PEXPSCH PRELSAL PTOTCH IDUM CDUM

O.OIX -0.01 I

g::;

-0.002 0.007 0.376 -0.324

INTERCEPT BYTEST FEMALE BLACK HISPANIC INCOME FAMSIZE EDUCD EDUCM

I;:;; (0.;) (I.‘)) (1.3) (1.6)

-0.231 -0.7X’) -O.YS’ 0.004 0. I I’ 0.029 0.056

-0.072 O.h(ll 0.253 -0.002 -0.003

(2.0) (3.6) (1.5) (0.3) (0.Y)

-0.073 0.93 0.253 -0.OOI -0.001

1;::; (I.‘) (0.2) (0.7)

(0. I)

0.019 -O.Olh -0.006

(1.6) (0.7) (0.3)

0.017 -0.000 -0.005

(1.4) (0.4) (0.2)

(1.X) (1.5) (1.1)

0.007 0.332 -0.313

(1.X) (1.3) (1.3)

0.005 0.X0 -0.2w

(1.2) (1.1) (1.1)

0.556 -0.SY2

(2.3) (1.5) O.OIO -0.01 I

(2.5) (1.3)

2070 0.100

statistically

significant.

effect

ment. The explanation further

genuine

high school/elementary

natively.

work:

Principals

in the model

in the HSB

more

Principals’ independent

teaching

reflect

have fewer

(under

experience

years

a

Alter-

the

effect results

via teacher

current

(over of

principal’s

significant How-

suggest an

and motivation.

of teachers appointed tenure

increase

(PTOTCH),

The during the

of

appointed

10%

in

the

by the current

gain score by between specification,

percentage principal,

Columns ADUML

representing

excellence

(2)

and

indicating

of

An

teachers

raises the mean

0.05 and 0.09, dcpcnding

on

an increaac of between

and 13% in the sample mean GAIN

Both

current

achievement.

across specifications selection

I4 years)

their

has no statistically on student

and

0. I03

higher are students’ gain scores, ceteris parihrs.

years of

5 on average) school data.

as principal

greater the percentage the

it may

specification.

sample

experience

school (PEXPSCH)

impact

is not clear

school difference,

than those in the ES elementary

ever.

achieve-

student

it may be peculiar to the sample used here.

administrative rather

empirical

2070

2070 0. I03

for this difference

without

or a difference

on

Y.66 -0. IX6

(7.‘)) (11.3) (1.2) (2.2) (3.4) (0.5) (I.‘)) (1.3) (1.6)

ADUMH ADUML PTACH PTACI. Observations Adjusted R’

value t statistics)

(5)

include

if the principal

7

score. ADUMH

and

ranked academic

as a high or low school goal, respectively.

coefficients

have the expected

sign and are

fairly large. Columns

(3) and (6) show the effect of

interacting

with these goals. The

PTOTCH

are in line with prior expectations. percentage high

of teachers appointed

academic

goals

by a principal

(PTACH)

student test score gains; the greater of

teachers

appointed

by

results

The greater the

higher

the with are

the percentage

a principal

with

low

Principals

and Student

287

Outcomes

Table 2. Continued.

5.71 -0. IX7 -0.37 ~O.XZZ -0.96-t 0.002 0. I03 0.037 0.05s

-0.06s

(1.9) (1.9)

(0.2) 0.7)

O..~Sh 0. IX6 -0.001 -0.002 0.062

-0.070 0.312 O.IXI -().()()I -0.002 0.065

(2.0) (1.X) (I .O) (0. I) (0.3) (1.7)

(0.S) (O.(l) (0.6) (3.6) (2.4) (1.7) (1.3)

0.01 I -0.017 -0.015 I .(12X 0.009 0.400 -0.312

(0.0) (0.X) (0.7) (3.1)

0.00x -0.010 -0.014 I .66X 0.00x 0.35 I -0.2hl

(0.7) (0.5) (0.7) (3.5) (1.‘)) (1.3) (1.1)

0.103 -0.613

(2.0) (1.5) O.OOY

(2.2) (1.X)

-0. IS7 -0.210 -0.777 -0.97s 0.003 0.007 0.031 0.05(1

CI.ASS SCHSIZE PPDEXP PCTLOW TEXP TSALARY

-0.071 O.-II2 0.145 0.001 -0.00I 0.062

(2.0) (2.2)

PTEACH PADMIN PEXPSCH PRELSAL PTOTCH IDUM CDUM

0.0I0 -0.012 -0.012 I .hc)S 0.00’) O.-L-t2 -0.313

S.hO

5.78 -0. IX7 -0.132 -0.86s -0.975 O.OOI 0. IO4 (I.031 0.055

io.sj

(0. I)

ADUMH ADUML PTACH PTACL

All models

academic score

‘070 0. I05 also include

INCUN.

goals (PTACL)

gains.

in PTOTCH

Thus

the total

DEDUN

2070 0. 107

(0.3) (0.4) (1.7)

IfG (1.3)

the lower are student effect

20%.

I;:!; (0.3) (1.7) (1.1) (1.6)

2070 0. I08

and MEDUN

test

of a IO”/, increase

by a principal with high academic goals while for principals with low academic goals it is actually negative.” This evidence suggesting a principal’s school goals affect student achievement raises important questions about how these goals are formed. and whcthcr there is any systematic relationship between a principal’s goals and their personal or school characteristics. One possibility. for example. is that ADUMH and ADUML are endogenous: high quality students bring about an emphasis on academic achievement. rather than l’ice wr.w. There is weak evidence in the HSB data that this may be the case. Maximum likelihood probit school level models. relating the probability that a principal had is over

(IS))

-0.013

Observations Adjusted R’

(3.5) (11.1) (1.2)

(3.5) (l-1.4) (I.‘) (2.5) (3.4) (0.3) (1.7) (1.1) ( I .6)

(3.4) (l-1.3) (I.?) (2.2) (3.4) (0.4) ( I .h) (1.4) ( I .h)

INTERCEPT BYTEST FEMALE BLACK HISPANIC INCOME FAMSIZE EDUCD EDUCM

high (low) academic goals to school and principal characteristics, were estimated. The results (available from the author upon request) show that while some school characteristics, including the mean gain score for students in the school. are statistically significant. the predictive power of the models is extremely low. Consequently. it is impossible to obtain good instruments for ADUMH and ADUML which can be used in the GAIN score models to correct for the endogeneity problem. Further research is undoubtedly merited to investigate the interrelationships between goal setting and student achievement. In addition to these findings on goal setting and teacher selection, there is some weak evidence that instructional leadership impacts student achievement. Principal influence over instructional methods

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(IDUM) has a positive effect on achievement, and influence over curriculum content (CDUM) a negative effect, but only the coefficient of IDUM in columns (4) and (5) is significant. This result contrasts with the findings of ES. whose variable measuring “instructional leadership” was estimated to be positively and significantly related to student test scores. This finding may reflect the rather different variable construction used in the two papers: for example, only a principal’s own assessment of their role is used to construct IDUM and CDUM. It is also possible that the finding reveals an important difference in the effects of elementary and high school principals. It is perhaps reasonable to believe that high schools. with greater teaching specializations, provide less scope for principal influence over classroom practices and curriculum content. The models including mean teacher salary (TSALARY) and the principal’s salary relative to teachers (PRELSAL) in columns (4), (5) and (6). show that both have positive effects on student achievement gains. [Inclusion of these variables decreases the magnitude of the coefficients of experience variables, and per pupil district expenditure, as compared with models (1). (2) and (3). This is to be expected given that teacher and principal experience are a major determinant of salaries]. The estimates imply that increasing the principal’s relative salary by 5% raises the mean student’s gain score by about 2(X6.” This is a significant effect, and of potential interest to policy makers. Without further research, however, the interpretation of the finding is problematic since there are several possible explanations consistent with the positive coefficient on PRELSAL. First, consistent with an efficiency wage story. the salary measure could reflect either unobserved principal quality, or that higher salaries motivate principals to work harder. Second, it is consistent with a selection process whereby better paid principals are selected by or assigned to schools with high performing students.” It is hoped that subsequent research into the role of administrator salaries will shed more light on this issue.

Review V. CONCLUSIONS

The analysis in this paper confirms for high school students the earlier finding of Ebcrts and Stone (198X) relating to elementary schools: principals “matter” for student achievement gains. In contrast to this earlier work. however. principal effects arc modelled somewhat differently. The HSB data used hcrc allow the impact of principals via the teacher selection process. and through goal setting, to be measured. These channels of principal influence are found to have a sizeable. statistically significant. effect on student achievement gains. In addition. higher principal salaries seem to be associated with better student performance. These results suggest the need for further rcsearch. Replication of this analysis (and that of Eberts and Stone) is undoubtedly merited. In particular, it would be useful to know whether the results found here are unique to high schools, and to what extent they are sensitive to the sample and model specification adopted. In addition. several of the findings reported deserve closer scrutiny. For example, the principal’s selection of teachers. the formation of principal’s school goals. and the role of all have potentially important principal salaries. policy significance and merit further attention. AcknowledgementsI amindebted to Ronald Ehrenberg. David Monk. and Dan Goldhnher for helpful comments. This paper has heen prepared as part of the research program of the Finance Center of the Consortium for Policy Research in Education (CPRE). a consortium of the University of Southern California. Rutgers University, Cornell University. Harvard University. Michigan State University. Stanford University and the University of Wisconsin-Madison. The work was partially supported by grant no. RI 17XGlOO39 from the U.S. Department of Education. Office of Educational Research and Improvement. The views expressed are those of the author and are not necessarily shared by USC. CPRE or its partners. or the U.S. Department of Education. An earlier version of this paper was presented at the American Education Finance Association Conference. New Orleans. LA. 19-22 March 1992. I am grateful to participants at this conference. especially Betty Malen. for their suggestions. None of the above. however. are responsible for any error\ that remain.

NOTES

I.

In an earlier version of this paper I attempted to assess the effect of clear school goal\ (a by both principal and teachers) on student achievement. independent of the type of goal

perceived

espoused.

Principals

and Student

Outcomes

Neither this. nor disagreement between teachers and principals over whether the school goals were clear. were statistically significant in educational production functions similar to those shown in Table 7. 2. 1 attempted to as’ress the effect of consultation on student achievement in an earlier version of this paper. I included the average of principal and mean teacher agreement with the statement that “staff arc involved in decision making” in educational production functions 4milar to those shown in Table 2. Although there were differences in levels of significance across specifications, the coefficient of this consultation vnriahle was alway negative. suggesting that greater consultation decreased student test score gains. 3. Proposal\ for ‘.school-hased management” are not alway consistent with regard to principals. On for principals over teachers and other the one hand. strong Ieader\hip. with greater authority re\oul-ccs. is called for. On the other hand. parental and teacher involvement in decision making is stressed (see. for example. Moore. IYYO). Note that Brcdeson (19X9) finds that excellent principals claim not to he threatened hy teacher “empowerment”. 1. The literature is commonly termed “effective schools” research. with “effective” generally referring to academic performnncc. See Purkeq and Smith (lY83). Concoran (19X.5) and Rosenholtz (IYXS) for reviews. Further insight can he garnered from “how to” guides designed to assist in training of principals (Lane and Walherg. IYX7: Blumherg. IYXY). and (auto)hiographicaI accounts of the day to 1987; Morris rt rtl.. IYX1). day experiences of administrators (Donaldson. IYY I : Sergiovanni. 5. Chubb and Moe (IYYO). using HSB. find their measure of school organization to be a significant and the esteem with which principals determinant of student achievement. Principal\‘ motivation. hold their teachers. nlonq with teacher professionalism and staff harmony. are components of this index, Even if this result\s correct. however. it is problematic to draw inferences for policy directly relevant to principals (see Witte. IYYO. for ;I critique of Chubb and Moe’s methodology). Similarly. Bryh and Driscoll (IYXX) use a “communal school index” of 23 separate HSB items that include teacher/;~dmlnistrator agreement on school discipline and on students’ learning capabilities. and perceptions of staff hek (lY7Y) discusses the relative merits of the basic model. Currently popular alternative are hierarchical linear models (HLMs). in which estimated “within school” parameters are used as outcomes in school level equations (-between schools”). It is far from clear. however. that the small gains in statistical efficiency achieved using this approach merit the computational cost. A discussion of relev;lnt ht;ltistical irsues can he found in. for example. Aitkin and Longford ( lYX6). Raudenhush ( IYXY) and Montmarquette and Mahseredjinn (IYXY). IO. It i\ po\\ihle to control for selection into private and public hchoola. but this presupposes an ability to specify the determinants of this decision. It i< simpler to confine attention to public schools. In addition. the primary interest IICI-c is in public policy resource allocation decisions. for which private schools iire not directI\ relevant. I I. 11.53 sophomore observations were put on the IYX6 HSB data tapes used here I I .724 of whom were in public schoolc. Of the latter around 3000 had dropped out of high school. graduated early or transfcrrcd school\ hy IYXZ. This leave5 approsimately X500 students for whom a gain score can he calculated. Unfortunately. principals and teacher\ were surveyed in only 320 or so public schools from the original IOOO. which dramatically cuts the number of available observations. Large numbers of missing values for certain variable> further reduces the sample size. School officials provided (19X0) information for the variables SCHSIZE. PPDEXP. TEXP used here. Additional data is constructed from the 19X-I ATS >urbcy. hy taking the mean response to questions on class size (CLASS) and salary (TSALARY) ot the teacher\ actually surveyed. Checks on the representativeness of this data (pcl-fbrmed by comparing c;dculated means with official’\ rc\pon
289

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Review

A test of the null hypothesis that GAIN is drawn from random sample with a normal distribution, cannot he rejected. 13. This approach yields identical coefficient estimates for all variables (except BYTEST) as a specification which models follow up scores as a function of base year score (such as that used by ES). The rationale for including BYTEST as an additional explanatory variable in the GAIN specification is that gain scores are as likely to be influenced by prior achievement (see Chubb and Moe. 1990, p. 117). If there is a “ceiling effect” students who score higher in the base year are expected. cetrris pnrihs, to make lower gains in achievement. Since BYTEST itself is partially explained by school variables. it cannot be considered truly exogenous. Unfortunately. since there is nothing. that influences BYTEST but not GAIN (at least o priori). one cannot use an instrumental variables approach. This implies that estimated school effects in the GAIN model may be understated. In their production functions. ES also included the square of the initial test score to capture possible nonlinearities. Models similar to those reported in Table 2 yielded insignificant and small positive coefficients on BYTEST squared. 14. Selectivity corrected models were estitnated for each of the specification\ pre\ented in section IV. controlling for the probability that a student drops out of high school hetbveen sophomore and senior years. using the Heckmnn two step procedure (Heckman. lY7Y). The correction does not significantly affect coefficient estimates for the variables of intcrcst in this study and hence are not shown. Work by Ehrenberg and Brewer (forthcoming) suggests differential effects by race of student. IS. An additional variable often included tn production functions is the highest teacher degree Icvcl. While HSB does contain data on the percentage of teacher\ with MAs and PhDs in each school. this was not used in the specifications presented here for two reasons. First (and inexplicably) principals’ highest degree level is not available. and second. initial estimates indicated that the teacher degree variable was small, negative and never significant. 16. The possibility of a non-linear relationship between experience measures (PTEACH. PADMIN. PEXPSCH) and clas!, size (CLASS). and student achievement was also investigated by adding quadratic terms to the models. In general. these were negative as expected but ncvcr stati\ticnlly significant. Consequently. specifications with these variables are not shown. 17. This is an approximate measure. since HSB contains data only on the percentage of faculty with les\ than 3 years. 3 to 10 years. and more than IO years at the current school. IX. Principals were asked in ATS to rank eight school goals in terms of importance (with no duplication permitted): basic literacy skills: citizenship: specific occupational skills: good work habit\ and \elf discipline: academic excellence; personal growth and fulfillment: human relation\ skills: moral and religious values. ADUMH = I if the principal ranked academic excellence as most important (i.e. I out of 8). ADUML = I if the principal ranked academic excellence as Ien reported in Table 2. IY. IDUM = I and CDUM = I. if the principal thought they had :I great deal of influence (i.e. replied with a 6 on a l-6 scale) over instructional methods and curriculum content rrspectivcly. and = 0 otherwise. Results reported in Table 2 do not change prcatly if these dummies are redefined to. for example. include ;I principal response of 5 or 6 to these qucstlons. Teachers were not. unfortunately. asked for their views on the principal’s role on these specific matters, Previous literature has generally indicated that it is the principal’s own perception of their role as instructional leader that is correlated with effective schooling. 20. 60 of the I47 production function studies reviewed by Hanushek (lY86. p. 1161) included teachel salary as an explanatory variable. 21, Relative principal salary is used in the analyhes to reduce collinearity between principal and mean teacher salary (TSALARY). Preliminary estimates usin? principal salary directly yielded a positive and significant coefficient on this variable across productlon function specification\. but its inclusion led to a significant negative coefficient on TSALARY. 22. Formal F tests of the hypothesis that the included principal variables are jointly equal to zero is easily rejected for each specifiation. The adjusted R’ estimates indicating the fit of the model. are small but not atypical. If the follow-up test score (FUTEST) is used as the dependent variable then the estimated coefficients are identical (except for that on BYTEST which is simply the coefficient obtained in the GAIN model plus one). However. the adjusted R’ is 0.75 for models (l)-(5). and 0.76 for model (6). 23. Since GAIN = FUTEST - BYTEST. the estimates imply that \vith FUTEST as the outcome variable the coefficient on BYTEST would be (I-O. ISh) for specification\ (l)-(3). This implie\ a loss of knowledge through time. holding all else constant. This is similar to the result obtained (but not discussed) by ES. This finding may he due to the fact that \ome \tudenth are not required to tnkc

Principals

and Student

Outcomes

mathematics or English courses during the I Ith and 12th grade. with a consequent deterioration of knowledge. Alternatively. it may simply be a reflection of the particular tests constructed for HSB. 24. Although these results are not the focus of interest in this paper it is worth noting that the estimated coefficients are fairly large. For specification (4) for example. a decrease in class size of 10% (2.3 students given the sample mean of 23) increases the gain score by 0.163. At the mrcrn gain score (0.6Y) Increase. Similarly. an increase of IO”% in the size of the this represents a 24”& [(2.3 x 0.071)/0.69] student body (about 12Y students in this sample) increases the mean gain score by about 7.5%. Of course. these percentage figures apply only for the mean gain score. 25. In column (3). for example, the total effect of a 10% increase in PTOTCH for a principal with high academic goals (0.05 + 0. IO) is to increase the mean gain score (0.69) by about 22%. For a principal with low academic goals the net effect of a 10% increase in PTOTCH is to lower the mean gain score by about 7% [(O.OS - 10)/0.69]. 26. An increase of 0. I in PRELSAL (about 5% at the sample mean) raises the gain score by about 0.16. which is just over 20% at the mean gain score of 0.6Y. 77. I owe this point to a referee. REFERENCES AIIKIN. M. and LONC;FORD, N. (1986) Statistical modelling issues in school effectiveness studies. J. R. Start. Sot.. Series A. 149 (Part Il. l-43. BARTELL, C.A. (1990) Out&nding‘secondary school principals reflect on instructional leadership. The H$t Scl~ool J. 73. 118-128. BUIMHERG. A. (1989) School Administration ns u Cruft: Fow~dations of Practice, Boston: Allyn and Bacon. BREDE~ON. P.V. (1989) Redefining leadership and the roles of school principals: responses to changes in the professional worklife of teachers. The High School J. 73, 9-20. BRYK. A.S. and DRISCOL.~., M.E. (1988) The School as Community: Contextual Influences. and Consequences for S/udents und Teachers. Madison. Wisconsin: National Center on Effective Secondary Schools, November 1988. BRYK. A.S.. LEE. V. and SMI.I.H, J. (1989) High School Organization and its Effects on Teachers and S~udentst An lnterprefative Summary of the Research. Paper presented to a conference on “Choice and Control in American Education”. University of Wisconsin-Madison. May IYXY. CARD. D. and KRUE(;ER, A. (1992) Does school quality matter? Returns to education and the characteristics of public schools in the United States. J. PO/it. Econ. 100, l-40. CIiLIBrs. J.E. and MOE. T. (IYYO) Politics, Markets and America’s Schools. Washington, DC: The Brookings Institution. COLEMAN, J.S.. et cd. (1966) Equnlity of Educational Opportunity. Washington, DC: U.S. Department of Health. Education. and Welfare. CONC.ORAN. T. (1985) Effective Secondary Schools. In Researching for Excellence: An Effective School:, Sourcehook (Edited by Kyle, R.M.). Washington. DC: E.H. White and Comoanv. i DE BEVOISE, W. (19X4) Synthesis of research on-the principal as instructional leaier. Educ. Lecrdership 41, 14-20. DONALDSON, G.A. (1991) Leurning to Lend: The Dynamics of High School Principalship. Westport, CT: Greenwood. EI~~.R~-s, R.W. and STONE, J.A. (1988) Student achievement in public schools: do principals make a difference? Econ. Educ. Rev. 7, 291-29’). EHRFNRER~;. R.G. and BREWER. D.J. (forthcoming) Do school and teacher characteristics matter? Evidence from High School and Beyond Econ. Educ. Rev. 13. FFRGUSON. R.F. (1991) Paying for public education: new evidence on how and why money matters. HrrrLwrd J. Legislation 28, 465-498. FIRESIONF. W.A. and HERRKU. R.E. (lY82) Prescriptions for effective elementary schools don’t fit secondarv schools. Educ. Leudershin 40. FIRESION~.’ W.A. and WILSON, B.L. (1985) Using bureaucratic influence and cultural linkages to improve instruction: the principal’s contribution. Educ. Administrution Ounrterlv 21, 7-30. H,ILLIN(;FK, P. and MURPHY, J.. (1987) Assessing and developing principal inst&tional leadership. Eriw. Lerrdership 45, 54-61 H,\NI!SHEK. E.A. (lY7Y) Conceptual and empirical issues in the estimation of education production functions. .I. Human Resources 14, 351-388. HANCISITEK. E.A. (1986) The economics of schooling: production function and efficiency in the public schools’? J. Econ. Liferature XXIV, 1141-l 17X. HEC‘KLIAN, J.J. (lY7Y) Sample selection bias as a specification error. Econometrico 47. 153-162.

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